Abstract: An image processing apparatus, system, and method to automatically determine a plurality of image acquisition settings for an scene; acquire a set of images of the scene, the set of images including multiple images acquired with a distinct plurality of the determined image acquisition settings; and generate a single image by combining the acquired set of images.

Abstract: In some embodiments, provided are procedures for processing images that may have different font sizes. In some embodiments, it involves OCR'ing with multiple passes at different resolutions.

Abstract: Methods and systems to for generating high accuracy estimates of the 3D orientation of a camera within a global frame of reference. Orientation estimates may be produced from an image-based alignment method. Other orientation estimates may be taken from a camera-mounted orientation sensor. The alignment-derived estimates may be input to a high pass filter. The orientation estimates from the orientation sensor may be processed and input to a low pass filter. The outputs of the high pass and low pass filters are fused, producing a stabilized video sequence.

Abstract: Embodiments of an apparatus, system and method are described for an edge-preserving smooth filter for low power architecture. A weighted pixel sum may be determined based on a weight of a central pixel and a weight of one or more neighboring pixels. The weight sum for the central pixel may be set to a power of two. An output of the central pixel may be displayed based on the weight sum and the weighted pixel sum. Other embodiments are described and claimed.

Abstract: Briefly, in accordance with one or more embodiments, an image-processing system is capable of receiving an image containing text, applying optical character recognition to the image, and then audibly reproducing the text via text-to-speech synthesis. Prior to optical character recognition, an orientation corrector is capable of detecting an amount of angular rotation of the text in the image with respect to horizontal, and then rotating the image by an appropriate amount to sufficiently align the text with respect to horizontal for optimal optical character recognition. The detection may be performed using steerable filters to provide an energy versus orientation curve of the image data. A maximum of the energy curve may indicate the amount of angular rotation that may be corrected by the orientation corrector.

Abstract: Briefly, in accordance with one or more embodiments, an image processing system is capable of receiving an image containing text, applying optical character recognition to the image, and then audibly reproducing the text via text-to-speech synthesis. Prior to optical character recognition, an orientation corrector is capable of detecting an amount of angular rotation of the text in the image with respect to horizontal, and then rotating the image by an appropriate amount to sufficiently align the text with respect to horizontal for optimal optical character recognition. The detection may be performed using steerable filters to provide an energy versus orientation curve of the image data. A maximum of the energy curve may indicate the amount of angular rotation that may be corrected by the orientation corrector.

Abstract: Methods and systems to create an image in which objects at different focal depths all appear to be in focus. In an embodiment, all objects in the scene may appear in focus. Non-stationary cameras may be accommodated, so that variations in the scene resulting from camera jitter or other camera motion may be tolerated. An image alignment process may be used, and the aligned images may be blended using a process that may be implemented using logic that has relatively limited performance capability. The blending process may take a set of aligned input images and convert each image into a simplified Laplacian pyramid (LP). The LP is a data structure that includes several processed versions of the image, each version being of a different size. The set of aligned images is therefore converted into a corresponding set of LPs. The LPs may be combined into a composite LP, which may then undergo Laplacian pyramid reconstruction (LPR). The output of the LPR process is the final blended image.

Abstract: Briefly, in accordance with one or more embodiments, an image processing system is capable of receiving an image containing text, applying optical character recognition to the image, and then audibly reproducing the text via text-to-speech synthesis. Prior to optical character recognition, an orientation corrector is capable of detecting an amount of angular rotation of the text in the image with respect to horizontal, and then rotating the image by an appropriate amount to sufficiently align the text with respect to horizontal for optimal optical character recognition. The detection may be performed using steerable filters to provide an energy versus orientation curve of the image data. A maximum of the energy curve may indicate the amount of angular rotation that may be corrected by the orientation corrector.

Abstract: Methods and systems to for generating high accuracy estimates of the 3D orientation of a camera within a global frame of reference. Orientation estimates may be produced from an image-based alignment method. Other orientation estimates may be taken from a camera-mounted orientation sensor. The alignment-derived estimates may be input to a high pass filter. The orientation estimates from the orientation sensor may be processed and input to a low pass filter. The outputs of the high pass and low pass filters are fused, producing a stabilized video sequence.

Abstract: Embodiments of an apparatus, system and method are described for an edge-preserving smooth filter for low power architecture. A weighted pixel sum may be determined based on a weight of a central pixel and a weight of one or more neighboring pixels. The weight sum for the central pixel may be set to a power of two. An output of the central pixel may be displayed based on the weight sum and the weighted pixel sum. Other embodiments are described and claimed.

Abstract: A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. The image reconstruction methodology may be used to enhance the image quality of images or video captured using a low resolution image capture device. Other embodiments are also described and claimed.

Abstract: Methods and apparatuses receive receiving a plurality of images and metadata associated with each respective image, determining a viewpoint of one of the images of the plurality, the viewpoint to represent the location and orientation of the image capture device when the image was captured and creating a view including the plurality of images, wherein placement of the images based on each images respective metadata and the determined viewpoint.

Abstract: In some embodiments, a method of processing a video sequence may include receiving an input video sequence having an input video sequence resolution, aligning images from the input video sequence, reducing noise in the aligned images, and producing an output video sequence from the reduced noise images, wherein the output video sequence has the same resolution as the input video sequence resolution. Other embodiments are disclosed and claimed.

Abstract: In some embodiments, provided are procedures for processing images that may have different font sizes. In some embodiments, it involves OCR'ing with multiple passes at different resolutions.

Abstract: Briefly, in accordance with one or more embodiments, an image processing system is capable of receiving an image containing text, applying optical character recognition to the image, and then audibly reproducing the text via text-to-speech synthesis. Prior to optical character recognition, an orientation corrector is capable of detecting an amount of angular rotation of the text in the image with respect to horizontal, and then rotating the image by an appropriate amount to sufficiently align the text with respect to horizontal for optimal optical character recognition. The detection may be performed using steerable filters to provide an energy versus orientation curve of the image data. A maximum of the energy curve may indicate the amount of angular rotation that may be corrected by the orientation corrector.

Abstract: A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. Other embodiments are also described and claimed.

Abstract: Various embodiments for performing digital video stabilization based on robust dominant motion estimation are described. In one embodiment, an apparatus may receive an input image sequence and estimate dominant motion between neighboring images in the image sequence. The apparatus may use a robust estimator to automatically detect and discount outliers corresponding to independently moving objects. Other embodiments are described and claimed.

Abstract: A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. The image reconstruction methodology may be used to enhance the image quality of images or video captured using a low resolution image capture device. Other embodiments are also described and claimed.

Abstract: A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. The image reconstruction methodology may be used to enhance the image quality of images or video captured using a low resolution image capture device. Other embodiments are also described and claimed.

Abstract: A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. Other embodiments are also described and claimed.